Midhun shah Hussain Senior Research Fellow
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Critical Questions to address
What are the biophysical interactions of phytoplankton and fluid dynamics in SCM layers ?
How to monitor the extent of distribution of Chl-a with changes in potential density on synoptic levels
How to describe bloom dynamics in physical terms?
How to track the changes in frequency domains of Chl-a in response to climate change
How to assess the forecasting effects and challenges in predicting
Chl-a dynamics
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What are the variations and its patterns?
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Study location, Algorithms and sampling
Observation & Methods
Study location, Algorithms and sampling
CTD spec & Probes

All the Algorithms, Analytics, Automations , Empirical modeling , spatial maps, simulations and documentations are done using R programming Language





An inverted gaussian can easily represent the SCM
Understanding Process and Interactions
Phytoplankton and Fluid dynamics
Understanding Process and Interactions
Four species were more focused due to its abundance in this location concurrent to BioArgo

- This disproportion in mass and volume makes the phytoplankton (especially diatoms) positively buoyant after a critical size range (Gross and Zeuthen, 1948)
Physical processes associated with Green Noctiluca Blooms
Surface-Subsurface oscillations of Chlorophyll-a
Physical processes associated with Green Noctiluca Blooms





The observations found that, surface currents have influence in lifting phytoplankton groups to surface waters. This is well evident from Empirical Orthogonal Teleconnections.
The lifing is more sensitive when startified waters with shallow MLDs are prominent
{.r-stretch}
The LVM models suggest that the Trophic dynamics are efficient in energy transfer
The nutrient dynamics on the subsurface are stable
The temperature and light factors are optimum
The subsurface blooms can be expected more frequent in future warming scenarios
\[ H_c \approx h_l \frac{\mu_0}{m} \]
Here \(H_c\) is critical depth, \(\mu_0\) is the phytoplankton population growth rate at the surface in the absence of any biomass loss, \(h_l\) is the light extinction coefficient and \(m\) is the biomass loss due to respiration, zooplankton grazing, viral lysis, and mortality which is assumed to be in constant depth.
Considering density (Sigma-t) rather than depth(critical depth)
According to the current study, any layer of strong density gradient in the upper layer is favorrable for phytoplankton \(d\sigma t_{max}\) , as it results in low settling velocity, which is expressed as follows
\[ d\sigma t_{max} \approx [P_z.T_z] \times \frac{Chl_z}{Vz} \]
\[ Chl_z = \frac{d\sigma_{max}}{P_z.T_z} \times V_z \]
Global density changes, settling velocities and time shifts in blooms
Phytoplankton response to climate change
Global density changes, settling velocities and time shifts in blooms

Warming -Stratification links to Noctiluca are well reflected in spatial distributions
The Northern Hemisphere seems to be subjected to more than south
The Land locked areas are more prone to the occurrence of Noctiluca blooms due to nutrient availability and efficient stratification












The wavelet decomposition in low frequencies (D7 and D8 waves) are strongly concurrent between the surface chl-a and pycnocline depth
The periods seems to be frequent within a range of 128 & 256 days
The changes in pycnocline are significant in NEAS because the nutrient -depleted surface waters enrich by pycnocline due to wind mixing
Wavelet Coherence: pycnocline depths vs surface chlorophyll
Wavelet Coherence: pycnocline vs SCM




Potential density Anomaly- \(kg/m^3\)
How SCM depth is controlled by SST-PDEN ratio

The SST-PDEN ratio relationship with SCM is utlized to derive Biologically Influenced Stratification Index (BISI)
BISI describes the probable SCM in locations, where the SST is increasing and density decreases
Limitations, possibilities and Applications
Forecasting submerged Chlorophyll with Machine Learning
Limitations, possibilities and Applications
\[
Chl_{max} = \frac{\sigma \times (-1.23)+ par \times (-0.0044) + 30.41 }{{{
{[{\frac{-D-D_{max}}{Chl_{spd}}}]}^2
}}}
\]



Subsurface Chlorophyll Maxima In The North Eastern Arabian Sea: Simulation On Impact Of Warming Midhun Shah Hussain, Smitha B. R, Mohamed Hatha Abdulla, M Sudhakar. Ecological Indicators, 2020
Subsurface Chlorophyll Maxima In The North Eastern Arabian Sea: Simulation On Impact Of Warming Midhun Shah Hussain, Smitha B. R, Mohamed Hatha Abdulla, M Sudhakar. Ecological Indicators, 2020
Subsurface Chlorophyll Maxima In The North Eastern Arabian Sea: Simulation On Impact Of Warming Midhun Shah Hussain, Smitha B. R, Mohamed Hatha Abdulla, M Sudhakar. Ecological Indicators, 2020
Role of mesoscale eddies in the sustenance of high biological productivity in North Eastern Arabian Sea during the winter-spring transition period. B.R., Smitha, Sanjeevan, V.N.,Padmakumar, K.B., Midhun Shah Hussain, Salini, T.C., Lix, J.K. Science of The Total Environment 2021
Subsurface Chlorophyll Maxima In The North Eastern Arabian Sea: Simulation On Impact Of Warming Midhun Shah Hussain, Smitha B. R, Mohamed Hatha Abdulla, M Sudhakar. Ecological Indicators, 2020
Role of mesoscale eddies in the sustenance of high biological productivity in North Eastern Arabian Sea during the winter-spring transition period. B.R., Smitha, Sanjeevan, V.N.,Padmakumar, K.B., Midhun Shah Hussain, Salini, T.C., Lix, J.K. Science of The Total Environment 2021
Subsurface Chlorophyll Maxima In The North Eastern Arabian Sea: Simulation On Impact Of Warming Midhun Shah Hussain, Smitha B. R, Mohamed Hatha Abdulla, M Sudhakar. Ecological Indicators, 2020
Role of mesoscale eddies in the sustenance of high biological productivity in North Eastern Arabian Sea during the winter-spring transition period. B.R., Smitha, Sanjeevan, V.N.,Padmakumar, K.B., Midhun Shah Hussain, Salini, T.C., Lix, J.K. Science of The Total Environment 2021
Ponman: An R Package For Bio-Argo Data Analysis Midhun Shah Hussain, Smitha B. R, Mohamed Hatha Abdulla SEANOE 2020
Collaborations
Publications from
Collaborations
Differentiation of two Chlorophthalmus species Chlorophthalmus corniger (Alcock, 1894) and C. acutifrons (Hiyama, 1940) based on otolith morphometry R Nikki, KV Kumar, K Oxona, MP Rajeeshkumar, KK Bineesh, Midhun Shah Hussain, H Manjebrayakath, N Saravanane, M Sudhakar. Indian Journal of Geo Marine Sciences, 2021
Faecal contamination and prevalence of pathogenic E. coli in shellfish growing areas along south-west coast of India. Ally C Antony, Reshma Silvester, PS Divya, PA Aneesa, Bini Francis, Midhun Shah Hussain, BT Umesh, Joy George, Mohamed Hatha Abdulla. Regional Studies in Marine Science 2021
Seasonal Dynamics of Major Phytoplankton Functional Types in the Coastal Waters of the West Coast of Canada Derived from OLCI Sentinel 3A. Perumthuruthil Suseelan, V., Xi, H., Belluz, J. D. B.,Midhun Shah Hussain., Bracher, A., & Costa, M. (1 C.E.).Frontiers in Marine Science, 2022.

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